Job Role: AI Architect (Agentic AI / Generative AI) Location: Toronto, ON (Hybrid) Duration: 12 Months of contract with Possible extension

Role Overview: We are seeking a highly experienced AI / GenAI Solution Architect with deep expertise in Agentic AI, LLM ecosystems, and enterprise AI architecture. The ideal candidate will lead the design and implementation of scalable, production-grade AI solutions, translating business needs into intelligent, automated systems using modern AI frameworks.

Required Qualifications: 10+ years of experience in: AI/ML, Solution or Enterprise Architecture Strong expertise in: Generative AI / LLMs (GPT, Claude, etc.), Agentic AI / AI Agents / Multi-agent systems, RAG (Retrieval Augmented Generation), API-led architecture & microservices Hands-on experience with: Azure / AWS / GCP, LLMOps / MLOps frameworks Proven experience delivering enterprise-scale AI solutions

Key Responsibilities:

1. Agentic AI Architecture & Strategy- Design and define architecture for agentic AI platforms and multi-agent systems Build frameworks for: Agent orchestration, Reasoning workflows, Memory and context handling, Tool usage and decision loops Establish reusable patterns for autonomous and semi-autonomous AI agents
2. Solution Design & Enterprise Integration- Architect and implement LLM-powered solutions integrated with: Enterprise applications, APIs and microservices, Knowledge management systems (RAG, vector databases) Design scalable, modular, and resilient architectures across hybrid cloud environments Enable interoperability between LLMs, orchestration layers, and enterprise systems
3. Generative AI & LLM Ecosystem- Develop solutions using GPT, Claude, and other foundation models Build and optimize: RAG pipelines, Prompt orchestration frameworks, LLM-based applications Drive LLMOps / MLOps practices including model lifecycle, monitoring, and optimization
4. Governance, Risk & Responsible AI- Implement Responsible AI frameworks covering: Explainability, Fairness, Transparency Define controls for: Agent permissions, Human-in-the-loop workflows, Data privacy and compliance Ensure adherence to security, regulatory, and enterprise governance standards
5. Platform Engineering & Operationalization- Design production-ready AI platforms with: Observability and monitoring, Performance tuning, CI/CD pipelines Enable scalable deployment of AI solutions using cloud-native architectures
6. Stakeholder Leadership- Partner with business, engineering, data, and product teams Lead discovery sessions to identify high-impact AI use cases Provide architectural guidance and AI strategy recommendations to leadership

Preferred Qualifications: Experience with:Workflow automation and orchestration tools, Enterprise integration patterns Regulated industries (banking, insurance, healthcare) Familiarity with: AI governance, compliance, and risk management Preferred Certifications: AWS / Azure / GCP AI or ML certifications TOGAF or equivalent architecture certification Security certifications (nice to have)